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Towards an optimal effect of government technology innovation policy mix: the case of government procurement and research and development subsidies with evidence from China

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  • Cao, Fuguo
  • Yang, Yusen
  • Guo, Shaobo
  • Appolloni, Andrea

Abstract

This study explores the optimal combination of government technological innovation policies by examining the interaction between government procurement and R&D subsidies in China. Recognizing the critical role of policy balance in enhancing innovation outcomes among companies, we investigate the effectiveness and efficiency of aligning these two policy instruments. Utilizing a two-party evolutionary game model, we analyze the strategic interactions between local governments and publicly listed companies (PLCs). Our numerical simulations and sensitivity analyses indicate that overreliance on R&D subsidies is suboptimal. Specifically, we find that PLCs—whether actively or passively engaged in innovation—respond more significantly to increases in government procurement than to R&D subsidies. When the value of government procurement surpasses that of R&D subsidies, a combination of both policies more effectively and efficiently stimulates PLCs innovation. Notably, government procurement can rapidly initiate PLCs’ innovation behavior in the early stages, reflecting its significant role in boosting innovation activity in the short term. These findings suggest that policymakers should recalibrate the innovation policy mix by emphasizing demand-side policies like government procurement and reducing reliance on R&D subsidies. This rebalancing is particularly crucial for economies like China, where there is a historical dependence on subsidies, to foster sustainable innovation growth.

Suggested Citation

  • Cao, Fuguo & Yang, Yusen & Guo, Shaobo & Appolloni, Andrea, 2025. "Towards an optimal effect of government technology innovation policy mix: the case of government procurement and research and development subsidies with evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 100(C).
  • Handle: RePEc:eee:soceps:v:100:y:2025:i:c:s0038012125000710
    DOI: 10.1016/j.seps.2025.102222
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